Acceptability of an existing online sexually transmitted and blood-borne infection testing model among gay, bisexual and other men who have sex with men in Ontario, Canada
Bibliographic record
Abstract
ObjectivesGay, bisexual and other men who have sex with men (GBM) are disproportionately affected by sexually transmitted and blood-borne infections (STBBI) due to stigma and other factors such as structural barriers, which delay STBBI testing in this population. Understanding acceptability of online testing is useful in expanding access in this population, thus we examined barriers to clinic-based testing, acceptability of a potential online testing model, and factors associated with acceptability among GBM living in Ontario.MethodsSex Now 2019 was a community-based, online, bilingual survey of GBM aged ≥15. Prevalence ratios (PR) and 95% confidence intervals (95%CI) were calculated using modified Poisson regression with robust variances. Multivariable modelling was conducted using the Hosmer-Lemeshow-Sturdivant approach.ResultsAmong 1369 participants, many delayed STBBI testing due to being too busy (31%) or inconvenient clinic hours (29%). Acceptability for online testing was high (80%), with saving time (67%) as the most common benefit, and privacy concerns the most common drawback (38%). Statistically significant predictors of acceptability for online testing were younger age (PR = 0.993; 95%CI: 0.991–0.996); a greater number of different sexual behaviours associated with STBBI transmission (PR = 1.031; 95%CI: 1.018–1.044); identifying as an Indigenous immigrant (PR = 1.427; 95%CI: 1.276–1.596) or immigrant of colour (PR = 1.158; 95%CI: 1.086–1.235) compared with white non-immigrants; and currently using HIV pre-exposure prophylaxis (PrEP) compared to not currently using PrEP (PR = 0.894; 95%CI: 0.828–0.965).ConclusionsAcceptability of online testing was high among GBM in Ontario. Implementing online STBBI testing may expand access for certain subpopulations of GBM facing barriers to current in-person testing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".